datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
video-game-super-resolutionLSDIR_SR_imagesLaion-Aesthetics-High-Resolution-GoT
Laion-Aesthetics-High-Resolution-GoT
Paper
Dataset Description
The Laion-Aesthetics-High-Resolution-GoT dataset is a collection of 3.77 million image-text pairs with rich grounding annotations. This dataset extends high-quality images from the LAION-Aesthetics collection with detailed text descriptions and object-level grounding information.
Key Features
Size: 3.77 million samples
Modalities: Image, Text, and Grounding Annotations
Image Resolution:… See the full description on the dataset page: https://huggingface.co/datasets/LucasFang/Laion-Aesthetics-High-Resolution-GoT.laion-high-resolution-chinese
laion-high-resolution-chinese
简介 Brief Introduction
取自Laion5B-high-resolution多语言多模态数据集中的中文部分，一共2.66M个图文对。
A subset from Laion5B-high-resolution (a multimodal dataset), around 2.66M image-text pairs (only Chinese).
数据集信息 Dataset Information
大约一共2.66M个中文图文对。大约占用381MB空间（仅仅是url等文本信息，不包含图片）。
Homepage: laion-5b
Huggingface: laion/laion-high-resolution
下载 Download
mkdir release && cd release
for i in {00000..00015}; do wget… See the full description on the dataset page: https://huggingface.co/datasets/wanng/laion-high-resolution-chinese.loaf_resolution_512from datasets import load_dataset
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
# 1. Load the dataset
# Note: Since this is a private repo, ensure you have run `huggingface-cli login`
repo_id = "bdanko/loaf_resolution_512"
print(f"Downloading {repo_id}...")
dataset = load_dataset(repo_id, split="train")
# 2. Grab the first example
example = dataset[0]
# Hugging Face automatically decodes the Parquet bytes into a PIL Image
img = example["image"]… See the full description on the dataset page: https://huggingface.co/datasets/bdanko/loaf_resolution_512.relaion-high-resolutionHRF-high-resolution-fundusarXiv:2501.18921https://arxiv.org/abs/2501.18921
git10m_resolution_le_16mpix_2.6m_datasetFANVID-Face_and_License_Plate_Recognition_in_Low-Resolution_Videos
FANVID: Face and License Plate Recognition in Low-Resolution Videos
Overview
FANVID is a benchmark dataset designed to advance research in face detection and matching and license plate recognition under challenging low-resolution surveillance video conditions. Unlike existing datasets, FANVID features faces and license plates that are unrecognizable in individual frames, encouraging models to leverage temporal context across video sequences for improved recognition.… See the full description on the dataset page: https://huggingface.co/datasets/kv1388/FANVID-Face_and_License_Plate_Recognition_in_Low-Resolution_Videos.sen2venus-super-resolutioneasyr1-60k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas
easyr1-60k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas
This dataset was generated using the EasyR1 grounding dataset pipeline.
Generation Details
Generated on: 2025-08-29 09:04:32 UTC
Script: push_easyr1_to_hf.py
Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data
Parameters Used
Maximum samples: 60000
Image resize (max megapixels): 4.0 MP
Minimum native image resolution: 0.0 MP
Prompt format: gta1
Output format:… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-60k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas.easyr1-49k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-5k-jedi-4MP
easyr1-49k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt-ui-vision-grounding-4MP-add-5k-jedi
Merged dataset composed of the following sources:
datasets/easyr1-44k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt-ui-vision-grounding-4MP (44769 samples in split train)
datasets/easyr1-21k-jedi-grounding-4MP-gta1-nores-fixed (18032 samples in split train)
Summary
Generated on: 2025-09-14 03:24:59 UTC
Split: train
Column strategy:… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-49k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-5k-jedi-4MP.AnyInstruct-resolution-1024easyr1-44k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-4MP
easyr1-44k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt-ui-vision-grounding-4MP
Merged dataset composed of the following sources:
datasets/easyr1-38k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt (38979 samples in split train)
datasets/ui-vision-grounding-4MP (5790 samples in split train)
Summary
Generated on: 2025-09-13 22:23:37 UTC
Split: train
Column strategy: intersection
Samples after merge: 44769
Usage
from… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-44k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-4MP.GenEval2_FLUX.2-dev_seed42_guidance4.0_steps50_resolution1024x1024_num_images1easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt
easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt
This dataset was generated using the EasyR1 grounding dataset pipeline.
Generation Details
Generated on: 2025-08-26 12:16:32 UTC
Script: push_easyr1_to_hf.py
Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data
Parameters Used
Maximum samples: 10000
Image resize (max megapixels): 4.0 MP
Minimum native image resolution: 0.0 MP
Prompt format:… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt.multi-camera-hsi-rgb-super-resolutionThis is a sample multi-camera dataset acquired by two sensors:
Ultris SR5 Camera - a hyperspectral imaging (HSI) sensor.
Raspberry Pi High Quality Camera - a classical RGB color sensor.
The dataset is used for pedagogical purpose in the course of Computer Vision (code 5PMSIVV3) at Grenoble INP Phelma, the application being RGB-guided HSI super-resolution.
The dataset was made possible thanks to the Multi-camera Imaging Research and Acquisition (MIRA) Platform of GIPSA-Lab, Grenoble, France.… See the full description on the dataset page: https://huggingface.co/datasets/mira-imaging/multi-camera-hsi-rgb-super-resolution.super_resolution_check
Music Demixing Benchmarks Super Resolution Checker for Music
Unofficial, unpacked mirror of MVSep Quality Checker Super Resolution Checker for Music dataset.
Super Resolution Checker for Music Leaderboard
Official zip file: 0.9 GB
Download
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huggingface_hub Python package
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individual files
all files in the repository
subsets… See the full description on the dataset page: https://huggingface.co/datasets/MusicDemixingBenchmarks/super_resolution_check.GenEval2_FLUX.1-dev_seed42_guidance4.0_steps50_resolution1024x1024_num_images1easyr1-38k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt
easyr1-38k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt
This dataset was generated using the EasyR1 grounding dataset pipeline.
Generation Details
Generated on: 2025-08-24 18:17:24 UTC
Script: push_easyr1_to_hf.py
Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data
Parameters Used
Maximum samples: 50000
Image resize (max megapixels): 4.0 MP
Minimum native image resolution: 0.0 MP
Prompt format: gta1
Output… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-38k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt.raw-56-burst-super-resolution
Dataset Description
This dataset comprises real-world scenes curated for research on raw burst super-resolution. It includes 56-frame low-resolution raw bursts and an aligned 8× high-resolution linear RGB reference image. This reference image is processed only with demosaicing to keep the original sensor characteristics.
The dataset contains 1,032 real-world handheld scenes captured with a Samsung Galaxy S23 Ultra smartphone. Data acquisition utilized the Camera2 API and… See the full description on the dataset page: https://huggingface.co/datasets/andersoncotrim/raw-56-burst-super-resolution.Image-and-video-super-resolution-dataeasyr1-20k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas
easyr1-20k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas
This dataset was generated using the EasyR1 grounding dataset pipeline.
Generation Details
Generated on: 2025-08-29 09:13:12 UTC
Script: push_easyr1_to_hf.py
Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data
Parameters Used
Maximum samples: 20000
Image resize (max megapixels): 4.0 MP
Minimum native image resolution: 0.0 MP
Prompt format: gta1
Output format:… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-20k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas.easyr1-10k-omniparser-prompt-ablation-qwen-tool-call-with-resolution-4MPeasyr1-10k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas
easyr1-10k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas
This dataset was generated using the EasyR1 grounding dataset pipeline.
Generation Details
Generated on: 2025-08-29 09:07:41 UTC
Script: push_easyr1_to_hf.py
Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data
Parameters Used
Maximum samples: 10000
Image resize (max megapixels): 4.0 MP
Minimum native image resolution: 0.0 MP
Prompt format: gta1
Output format:… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt-no-os-atlas.easyr1-70k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt
easyr1-70k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt
This dataset was generated using the EasyR1 grounding dataset pipeline.
Generation Details
Generated on: 2025-08-27 01:19:44 UTC
Script: push_easyr1_to_hf.py
Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data
Parameters Used
Maximum samples: 70000
Image resize (max megapixels): 4.0 MP
Minimum native image resolution: 0.0 MP
Prompt format: gta1
Output format: coordinates… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-70k-hard-qwen7b-easy-gta1-4MP-no-resolution-in-prompt.git10m_resolution_1.6m_0.5mpix_datasetgit10m_resolution_le_16mpix_datasetgit10m_resolution_1.1m_0.5mpix_datasetGenEval2_Qwen-Image_seed42_guidance4.0_steps50_resolution1024x1024_num_images1